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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국정보처리학회 JIPS(Journal of Information Processing Systems) JIPS(Journal of Information Processing Systems) 제11권 제4호
- 발행연도
- 2015.1
- 수록면
- 538 - 555 (18page)
이용수
초록· 키워드
오류제보하기
Event detection based on using features from a static grid can give poor results from the viewpoint of two main aspects: the position of the camera and the position of the event that is occurring in the scene. The former causes problems when training and test events are at different distances from the camera to the actual position of the event. The latter can be a source of problems when training events take place in any position in the scene, and the test events take place in a position different from the training events. Both issues degrade the accuracy of the static grid method. Therefore, this work proposes a method called a dynamic grid for event detection, which can tackle both aspects of the problem. In our experiment, we used the dynamic grid method to detect four types of event patterns: implosion, explosion, two-way, and one-way using a Multimedia Analysis and Discovery (MAD) pedestrian dataset. The experimental results show that the proposed method can detect the four types of event patterns with high accuracy. Additionally, the performance of the proposed method is better than the static grid method and the proposed method achieves higher accuracy than the previous method regarding the aforementioned aspects.
목차
등록된 정보가 없습니다.
참고문헌 (23)
참고문헌 신청
A. A. Sodemann / 2012 / A review of anomaly detection in automated surveillance / IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 42 (6)
A. Hampapur / 2008 / Smart video surveillance for proactive security / IEEE Signal Processing Magazine 25 (4) : 136 ~ 134
B. T. Morris / 2008 / A survey of vision-based trajectory learning and analysis for surveillance / IEEE Transactions on Circuits and Systems for Video Technology 18 (8) : 11
C. Piciarelli / 2011 / Surveillance-oriented event detection in video streams / IEEE Intelligent Systems 26 (3) : 32 ~ 41
D. Zhang / 2013 / Crowd abnormal behavior detection based on machine learning / Information Technology Journal 12 (6) : 1199 ~ 1205
A. Hampapur / 2008 / Smart video surveillance for proactive security / IEEE Signal Processing Magazine 25 (4) : 136 ~ 134
B. T. Morris / 2008 / A survey of vision-based trajectory learning and analysis for surveillance / IEEE Transactions on Circuits and Systems for Video Technology 18 (8) : 11
C. Piciarelli / 2011 / Surveillance-oriented event detection in video streams / IEEE Intelligent Systems 26 (3) : 32 ~ 41
D. Zhang / 2013 / Crowd abnormal behavior detection based on machine learning / Information Technology Journal 12 (6) : 1199 ~ 1205
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